{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# `Model` API: Quick start guide\n",
        "\n",
        "> **tldr:** This notebook is a quick-start guide to the NeuralGCM Model API, which is designed for model developers and built on `nnx` and `coordax`. You will learn how to define a model by subclassing `api.Model` and implementing the core `assimilate`, `advance`, and `observe` methods. The guide explains how models manage their own state (e.g., `Prognostic` variables) and how this state is partitioned using a typing system. This partitioning is essential for applying JAX transformations like `nnx.scan` (demonstrated in an `unroll_model` example) and `jax.grad`. Finally, it covers how `ObservationOperator` modules are used to compute and extract data from the model state in response to a user query.\n",
        "\n",
        "`Model` API is designed for model developers and aims to facilitate:\n",
        "* Training\n",
        "* Composition and coupling of model components\n",
        "* Inspection and model surgery\n",
        "\n",
        "Readers unfamiliar with [`coordax`](https://coordax.readthedocs.io/en/latest/) or [`nnx`](https://flax.readthedocs.io/en/v0.8.3/experimental/nnx/index.html) are encouraged to review the respective tutorials before proceeding with this notebook.\n",
        "\n",
        "Outline:\n",
        "1. Defining a model\n",
        "2. Understanding model state and running simulations\n",
        "3. Observation operators and model queries\n",
        "\n"
      ],
      "metadata": {
        "id": "tW_MyBy5E2Cs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import dataclasses\n",
        "from functools import partial\n",
        "from typing import Callable\n",
        "\n",
        "from flax import nnx\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import graphviz\n",
        "\n",
        "import jax_datetime as jdt\n",
        "import coordax as cx\n",
        "from neuralgcm.experimental.core import api\n",
        "from neuralgcm.experimental.core import coordinates\n",
        "from neuralgcm.experimental.core import observation_operators\n",
        "from neuralgcm.experimental.core import nnx_compat\n",
        "from neuralgcm.experimental.core import time_integrators\n",
        "from neuralgcm.experimental.core import parallelism\n",
        "from neuralgcm.experimental.core import transforms\n",
        "from neuralgcm.experimental.core import spherical_transforms\n",
        "from neuralgcm.experimental.core import typing\n",
        "from neuralgcm.experimental.core import module_utils\n",
        "\n",
        "\n",
        "# small formatting improvements\n",
        "import contextlib\n",
        "np.set_printoptions(threshold=100)\n",
        "\n",
        "@contextlib.contextmanager\n",
        "def print_error():\n",
        "  try:\n",
        "    yield\n",
        "  except Exception as e:\n",
        "    print(f'{type(e).__name__}: {e}')\n",
        "\n",
        "\n",
        "import logging\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "logging.getLogger('jax._src.lib.xla_bridge').addFilter(lambda _: False)"
      ],
      "metadata": {
        "id": "PNh6XimIK8gw"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Defining a model\n",
        "\n",
        "**Key concepts:**\n",
        "* A model is defined by implementing `assimilate`, `advance` and `observe` methods plus a `timestep` property\n",
        "* The state of NeuralGCM models is stored in different `nnx.Variable` subtypes\n",
        "* `Model` objects are instances of `nnx.Module`"
      ],
      "metadata": {
        "id": "CmDTsAaoK_Pl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining a `Model` requires implementing core methods for the following functions:\n",
        "1. `assimimilate(inputs)` - assimilates inputs into a model state\n",
        "2. `advance()` - advances model state by a `timestep`\n",
        "3. `observe(query)` - computes observations for user `query`\n",
        "4. `timestep` - model property indicating the size of a model timestep"
      ],
      "metadata": {
        "id": "kd0RzNurtL6y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lorenz-96 demo\n",
        "As a working example we will implement variations of a Lorenz-96 dynamical system. The two-scale model extends includes slow and fast variables that couple to one another, described by a system of ODEs:\n",
        "\n",
        "$$\n",
        "\\frac{dX_k}{dt} &= -X_{k-1}(X_{k-2} - X_{k+1}) - X_k + F - \\frac{hc}{b} \\sum_{j=1}^{J} Y_{j,k} \\\\\n",
        "\\frac{dY_{j,k}}{dt} &= -cbY_{j+1,k}(Y_{j+2,k} - Y_{j-1,k}) - cY_{j,k} + \\frac{hc}{b} X_k\n",
        "$$\n",
        "\n",
        "Here $h, c, b$ are coupling parameters. The term $B_k(Y) = \\frac{hc}{b} \\sum_{j=1}^{J} Y_{j,k}$ represents the feedback from fast scales to slow scales."
      ],
      "metadata": {
        "id": "QVmy-v_ngqbV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Definition of the Full Lorenz96\n",
        "\n",
        "IntegratorCls = Callable[[time_integrators.ExplicitODE, float], nnx.Module]\n",
        "\n",
        "\n",
        "def lorenz96_x_term(x: jax.Array, f: float) -> jax.Array:\n",
        "  \"\"\"Computes uncoupled term for `X` variables in L96 system.\"\"\"\n",
        "  x_p1 = jnp.roll(x, -1)\n",
        "  x_m1 = jnp.roll(x, 1)\n",
        "  x_m2 = jnp.roll(x, 2)\n",
        "  return -x_m1 * (x_m2 - x_p1) - x + f\n",
        "\n",
        "\n",
        "def lorenz96_y_term(y: jax.Array, c: float, b: float) -> jax.Array:\n",
        "  \"\"\"Computes uncoupled term for `Y` variables in L96 system.\"\"\"\n",
        "  y_p1 = jnp.roll(y, -1)\n",
        "  y_p2 = jnp.roll(y, -2)\n",
        "  y_m1 = jnp.roll(y, 1)\n",
        "  return -(c * b) * y_p1 * (y_p2 - y_m1) - c * y\n",
        "\n",
        "\n",
        "@nnx_compat.dataclass\n",
        "class Lorenz96(api.Model):\n",
        "  \"\"\"Two timescale Lorenz-96 model.\"\"\"\n",
        "  k_axis: cx.Coordinate\n",
        "  j_axis: cx.Coordinate\n",
        "  forcing: float | nnx.Param = 10.0\n",
        "  c: float | nnx.Param = 10.0\n",
        "  b: float | nnx.Param = 10.0\n",
        "  h: float | nnx.Param = 1.0\n",
        "  time_integrator_cls: IntegratorCls = time_integrators.RungeKutta4\n",
        "  dt: float = 0.002  # float integration time step for the Lorenz system.\n",
        "  operators: dict[  # additional observation operators that will be discussed.\n",
        "      str, observation_operators.ObservationOperator\n",
        "  ] = dataclasses.field(default_factory=dict)\n",
        "  # Prognostic fields initialized in __post_init__\n",
        "  x: typing.Prognostic = dataclasses.field(init=False)\n",
        "  y: typing.Prognostic = dataclasses.field(init=False)\n",
        "  time: typing.Prognostic = dataclasses.field(init=False)\n",
        "\n",
        "  def __post_init__(self):\n",
        "    ks, js = self.k_axis, self.j_axis\n",
        "    kjs = cx.compose_coordinates(ks, js)\n",
        "    self.x = typing.Prognostic(cx.wrap(jnp.zeros(ks.shape), ks))\n",
        "    self.y = typing.Prognostic(cx.wrap(jnp.zeros(kjs.shape), kjs))\n",
        "    self.time = typing.Prognostic(cx.wrap(jdt.Datetime(jdt.Timedelta())))\n",
        "\n",
        "  @property\n",
        "  def xy_prognostics(self):\n",
        "    return {'x': self.x.value, 'y': self.y.value}\n",
        "\n",
        "  @property\n",
        "  def prognostics(self):\n",
        "    return self.xy_prognostics | {'time': self.time.value}\n",
        "\n",
        "  @module_utils.ensure_unchanged_state_structure\n",
        "  def assimilate(self, inputs: dict[str, dict[str, cx.Field]]) -> None:\n",
        "    slice_last_time = lambda f: cx.cmap(lambda a: a[-1])(f.untag('timedelta'))\n",
        "    self.x.value = slice_last_time(inputs['slow']['x'])\n",
        "    self.y.value = slice_last_time(inputs['fast']['y'])\n",
        "    self.time.value = slice_last_time(inputs['slow']['time'])\n",
        "\n",
        "  @module_utils.ensure_unchanged_state_structure\n",
        "  def advance(self) -> None:\n",
        "    k, j = self.k_axis, self.j_axis\n",
        "    f, c, b, h = self.forcing, self.c, self.b, self.h\n",
        "    x_out_axes = {k: v for k, v in self.x.named_axes.items() if k != 'k'}\n",
        "    y_out_axes = {k: v for k, v in self.y.named_axes.items() if k != 'j'}\n",
        "    x_dot_fn = cx.cmap(partial(lorenz96_x_term, f=f), out_axes=x_out_axes)\n",
        "    y_dot_fn = cx.cmap(partial(lorenz96_y_term, b=b, c=c), out_axes=y_out_axes)\n",
        "    sum_fn = cx.cmap(jnp.sum)\n",
        "    # Equation for coupled X and Y variables.\n",
        "    all_terms_fn = lambda s: {\n",
        "        'x': x_dot_fn(s['x'].untag(k)).tag(k) - (h*c/b)*sum_fn(s['y'].untag(j)),\n",
        "        'y': y_dot_fn(s['y'].untag(j)).tag(j) + (h*c/b) * s['x'],\n",
        "    }\n",
        "    full_ode = time_integrators.ExplicitODE.from_functions(all_terms_fn)\n",
        "    next_xy = self.time_integrator_cls(full_ode, self.dt)(self.xy_prognostics)\n",
        "    self.x.value = next_xy['x']\n",
        "    self.y.value = next_xy['y']\n",
        "    self.time.value += self.timestep  # treated separately to avoid round off.\n",
        "\n",
        "  @module_utils.ensure_unchanged_state_structure\n",
        "  def observe(\n",
        "      self,\n",
        "      query: dict[str, dict[str, cx.Field | cx.Coordinate]],\n",
        "  ) -> dict[str, dict[str, cx.Field]]:\n",
        "    fixed_operators = {\n",
        "        'slow': observation_operators.DataObservationOperator({'x': self.x.value}),\n",
        "        'fast': observation_operators.DataObservationOperator({'y': self.y.value}),\n",
        "    }\n",
        "    result = {}\n",
        "    all_operators = fixed_operators | self.operators\n",
        "    for k, q in query.items():\n",
        "      if k in all_operators:\n",
        "        result[k] = all_operators[k].observe(self.prognostics, q)\n",
        "    return result\n",
        "\n",
        "  @property\n",
        "  def timestep(self) -> np.timedelta64:\n",
        "    # Lorenz96 non dimensional time roughly corresponds to 120h.\n",
        "    return np.timedelta64(int(self.dt * 120), 'h')"
      ],
      "metadata": {
        "id": "Q5TWM-fvSe51"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the model above included three main steps:\n",
        "\n",
        "1. Creation of `typing.Prognostic` variables (`self.x`, `self.y` and `self.time`) at initialization\n",
        "2. Implementation of core methods `assimilate`, `advance`, `observe`, `timedelta`\n",
        "3. Adding the optional `ensure_unchanged_state_structure` decorator, which verifies that these methods do not change the state's structure\n",
        "\n",
        "Now we can instantiate and play with this model. We will look into some implementation details shortly\n"
      ],
      "metadata": {
        "id": "EZxa9p0mZk2b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiating coordinates and Lorenz96 model.\n",
        "k = cx.LabeledAxis('k', np.arange(36))\n",
        "j = cx.LabeledAxis('j', np.arange(8))\n",
        "\n",
        "l96_model = Lorenz96(k, j)\n",
        "\n",
        "nnx.display(l96_model)  # inspecting model using treescope."
      ],
      "metadata": {
        "id": "xCCQq9nwZQ5v",
        "colab": {
          "height": 124
        },
        "outputId": "e2178be3-b1d3-4696-d86c-c827094ddf76"
      },
      "execution_count": 7,
      "outputs": [
        {
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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_fe17763d34474b95a538436a48535bc8\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# simulation state variables are currently initialized to 0.\n",
        "nnx.display(l96_model.x)"
      ],
      "metadata": {
        "id": "l0b8M2eaaQVU",
        "colab": {
          "height": 34
        },
        "outputId": "6334d398-37f4-4d19-ec45-4838ef2051df"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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oldScript.parentNode.replaceChild(newScript, oldScript); } if (root.state.loadingMsg) { root.state.loadingMsg.remove(); root.state.loadingMsg = null; } root.shadowRoot.appendChild(node); }; root.defns.insertContent = ((contentNode, compressed) => { if (compressed) { root.state.deferring = true; } if (root.state.deferring) { root.state.chain = (async () => { await root.state.chain; if (compressed) { const encoded = contentNode.textContent; const blob = new Blob([ Uint8Array.from(atob(encoded), (m) => m.codePointAt(0)) ]); const reader = blob.stream().pipeThrough( new DecompressionStream(\"deflate\") ).pipeThrough( new TextDecoderStream(\"utf-8\") ).getReader(); const parts = []; while (true) { const step = await reader.read(); if (step.done) { break; } parts.push(step.value); } const tpl = document.createElement('template'); tpl.innerHTML = parts.join(\"\"); _insertNode(tpl.content); } else { _insertNode(contentNode.content); } })(); } else { _insertNode(contentNode.content); } }); 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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1676139d505d40939d6424170075b252\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1676139d505d40939d6424170075b252\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using models' core methods.\n",
        "\n",
        "fig, ax = plt.subplots()  # will be used to plot X_k state.\n",
        "\n",
        "# Preparing inputs for model initialization\n",
        "kj = cx.compose_coordinates(k, j)\n",
        "rng = np.random.RandomState(0)\n",
        "x_init = abs(10 * np.sin(np.linspace(0, 13 * 2*np.pi, k.sizes['k'])))\n",
        "x_init[0] += 0.01\n",
        "t0 = jdt.Datetime.from_isoformat('2000-01-01')\n",
        "inputs = {\n",
        "    'slow': {'x': cx.wrap(x_init, k), 'time': cx.wrap(t0)},\n",
        "    'fast': {'y': cx.wrap(rng.uniform(size=kj.shape, low=-0.5, high=0.5), kj)},\n",
        "}\n",
        "# assimilate expects at least a dummy timedelta dimension, we add it here.\n",
        "t_del = coordinates.TimeDelta(np.zeros(1) * np.timedelta64(1, 'h'))\n",
        "add_td = lambda f: f.broadcast_like(cx.compose_coordinates(t_del, f.coordinate))\n",
        "inputs = jax.tree.map(add_td, inputs, is_leaf=cx.is_field)\n",
        "\n",
        "# Assimilate updates the simulation state in place using inputs.\n",
        "l96_model.assimilate(inputs)\n",
        "\n",
        "# Observe lets the user to \"query\" the simulation state.\n",
        "observations = l96_model.observe({'slow': {'x': k}})  # observe X.\n",
        "observations['slow']['x'].to_xarray().plot(x='k', ax=ax, label='i=0')  # plot X.\n",
        "\n",
        "# Advance updates the simulation state in place.\n",
        "for _ in range(500):\n",
        "  l96_model.advance()\n",
        "\n",
        "observations = l96_model.observe({'slow': {'x': k}})  # observe evolved state.\n",
        "observations['slow']['x'].to_xarray().plot(x='k', ax=ax, label='i=500')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "a9g8nRJDVK2a",
        "colab": {
          "height": 295
        },
        "outputId": "8488db9a-685e-49a5-fe0c-1268c7866d49"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x108c388bed20>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 370
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice how the system's state \"lives\" on the `Model` instance and is updated when\n",
        "we call methods such as `assimilate`, `advance`.\n",
        "\n",
        "We can see the effect of that in the example above by noticings that the prognostics variables `model.x` has changed:"
      ],
      "metadata": {
        "id": "9_M4GCPxn5Hj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nnx.display(l96_model.x)"
      ],
      "metadata": {
        "id": "SKPFBSAxptGN",
        "colab": {
          "height": 34
        },
        "outputId": "7f882215-9210-4875-8455-f13e49c260f1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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<treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1b3c4a80a6924f21955b1edc6cc0abdb\")) .filter((elt) => !elt.dataset['step0']) )[0]; root.dataset['step0'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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</script> <treescope-run-here><script type=\"application/octet-stream\"> const root = ( Array.from(document.getElementsByClassName( \"treescope_out_1b3c4a80a6924f21955b1edc6cc0abdb\")) .filter((elt) => !elt.dataset['step1']) )[0]; root.dataset['step1'] = 1; root.defns.insertContent( this.parentNode.querySelector('script[type=\"application/octet-stream\"]'), true ); this.parentNode.remove(); </script></treescope-run-here> </div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This choice offers several advantages:\n",
        "* No need to explicitly wire the simulation state between calls\n",
        "* Simulation state can be automatically extracted using `nnx.state`\n",
        "\n",
        "This also implies certain sharp edges:\n",
        "* Execution order matters, as the model is mutable, examples include:\n",
        "  * Changing parameter by assignment: `l96_coarse_model.forcing.value = 0`\n",
        "  * Running model methods like advance, assimilate,"
      ],
      "metadata": {
        "id": "-CWkwKI_ptJN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Understanding model state\n",
        "\n",
        "**Key concepts:**\n",
        "* Typing system allows partitioning of the model state (simulation, fixed params, trainable params, etc)\n",
        "* Model state partitioning is crucial for transformations like `scan`, `vmap`, `grad`\n",
        "* `Model` can be easily vectorized if simulation state variables wrap `cx.Field` instances"
      ],
      "metadata": {
        "id": "tPgOIABMo4xt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Model` instance can hold a variety of stateful parameters of different `nnx.Variable` subtypes.\n",
        "\n",
        "Below are the most common types showing their hierarchical relationships:\n"
      ],
      "metadata": {
        "id": "KwULwyLJyRET"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title types hierarchy graphviz\n",
        "dot_source = \"\"\"\n",
        "digraph {\n",
        "  rankdir=BT\n",
        "  node [shape=box, style=rounded, fontname=\"Arial\"]\n",
        "  edge [arrowhead=empty]\n",
        "\n",
        "  Variable [label=\"nnx.Variable\"]\n",
        "  DynamicInput\n",
        "  SimulationVariable\n",
        "  Prognostic\n",
        "  Diagnostic\n",
        "  Randomness\n",
        "  Param [label=\"nnx.Param\"]\n",
        "\n",
        "  Prognostic -> SimulationVariable\n",
        "  Diagnostic -> SimulationVariable\n",
        "  Randomness -> SimulationVariable\n",
        "  SimulationVariable -> Variable\n",
        "  DynamicInput -> Variable\n",
        "  Param -> Variable\n",
        "}\n",
        "\"\"\"\n",
        "\n",
        "# Render the graph in Colab\n",
        "graph = graphviz.Source(dot_source)\n",
        "graph"
      ],
      "metadata": {
        "cellView": "form",
        "id": "b5mHtHVp0Eki",
        "colab": {
          "height": 361
        },
        "outputId": "6954a8e6-1a2e-49f8-fdb8-b2149093915f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing Graphviz MPM in /export/hda3/borglet/remote_hdd_fs_dirs/0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639.14b334fb3717c109/mount/logs.0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639/tmp/tmp5wua_jrz_dot/graphviz\n",
            "Installed package tools/graphviz version live at /export/hda3/borglet/remote_hdd_fs_dirs/0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639.14b334fb3717c109/mount/logs.0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639/tmp/tmp5wua_jrz_dot/graphviz.\n",
            "Installing X11 Fonts MPM in /export/hda3/borglet/remote_hdd_fs_dirs/0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639.14b334fb3717c109/mount/logs.0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639/tmp/tmp5wua_jrz_dot/x11_fonts\n",
            "Installed package visualization/graphviz_server/x11_fonts version live at /export/hda3/borglet/remote_hdd_fs_dirs/0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639.14b334fb3717c109/mount/logs.0.colab.whirl_dkochkov_oe.kernel.dkochkov.1275925275639/tmp/tmp5wua_jrz_dot/x11_fonts.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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          "metadata": {},
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        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "We can extract slices of model state using `nnx.state` using specific types, groups of types or `nnx.filterlib.Filter`."
      ],
      "metadata": {
        "id": "gNUK7ju82rq0"
      }
    },
    {
      "metadata": {
        "id": "raonQkbPjjLM"
      },
      "cell_type": "code",
      "source": [
        "l96_model.h = nnx.Param(1.0)  # make `h` a dummy trainable parameter."
      ],
      "outputs": [],
      "execution_count": 19
    },
    {
      "cell_type": "code",
      "source": [
        "nnx.state(l96_model, nnx.Variable)  # all stateful Variable, includes `h`."
      ],
      "metadata": {
        "id": "o3WeNoUD2cCI",
        "outputId": "3b18e2be-307d-4bd6-8462-bd2c278f16e1"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State({\n",
              "  'h': Param(\n",
              "    value=1.0\n",
              "  ),\n",
              "  'time': Prognostic( # 2 (8 B)\n",
              "    value=<Field dims=() shape=() axes={} >\n",
              "  ),\n",
              "  'x': Prognostic( # 36 (144 B)\n",
              "    value=<Field dims=('k',) shape=(36,) axes={'k': LabeledAxis} >\n",
              "  ),\n",
              "  'y': Prognostic( # 288 (1.2 KB)\n",
              "    value=<Field dims=('k', 'j') shape=(36, 8) axes={'j': LabeledAxis, 'k': LabeledAxis} >\n",
              "  )\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nnx.state(l96_model, typing.Prognostic)  # only Prognostic variables."
      ],
      "metadata": {
        "id": "0V0_j7Bc2iz6",
        "outputId": "2e6c2c02-5b38-4550-fae4-441d5bbdbc59"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State({\n",
              "  'time': Prognostic( # 2 (8 B)\n",
              "    value=<Field dims=() shape=() axes={} >\n",
              "  ),\n",
              "  'x': Prognostic( # 36 (144 B)\n",
              "    value=<Field dims=('k',) shape=(36,) axes={'k': LabeledAxis} >\n",
              "  ),\n",
              "  'y': Prognostic( # 288 (1.2 KB)\n",
              "    value=<Field dims=('k', 'j') shape=(36, 8) axes={'j': LabeledAxis, 'k': LabeledAxis} >\n",
              "  )\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The ability to partition model state becomes critical for several tasks:\n",
        "1. Defining transformations on functions that operate on a `Model` instance\n",
        "2. Working with functional API"
      ],
      "metadata": {
        "id": "4uVVGXmc5330"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using model StateAxes to define unroll_model with `scan`\n",
        "\n",
        "In the following example, we will implement a function that uses `model.advance()` and `model.observe(...)` to efficiently run a simulation using `Model` API. To accomplish this we must specify which parts of the model state are \"carried\" by the scan (the simulation state) and which are closed over (the parameters). Correctly partitioning the state this way is critical for performance and avoiding excessive memory usage."
      ],
      "metadata": {
        "id": "CvPPJ-sb85wx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Implementation of model_unroll using `nnx.scan`\n",
        "\n",
        "@nnx.jit(static_argnums=(1, 3))\n",
        "def unroll_model(\n",
        "    model,\n",
        "    outer_steps: int,\n",
        "    query,\n",
        "    inner_steps: int = 1,\n",
        ") -> tuple[typing.ModelState, typing.Pytree]:\n",
        "  \"\"\"Runs simulation calling `model.observe(query)` every `innfer_steps`.\"\"\"\n",
        "\n",
        "  def _inner_step(model):\n",
        "    model.advance()\n",
        "\n",
        "  # Through all scan transformations we want to iterate on simulation variables\n",
        "  # by treating them as scan \"Carry\" values. The rest of the model state should\n",
        "  # be closed over to avoid the possibility of unwanted replication.\n",
        "  model_axes = nnx.StateAxes({typing.SimulationVariable: nnx.Carry, ...: None})\n",
        "  inner_step_fn = nnx.scan(\n",
        "      _inner_step,\n",
        "      length=inner_steps,\n",
        "      in_axes=model_axes,\n",
        "      out_axes=0,\n",
        "  )\n",
        "\n",
        "  def _step(model):\n",
        "    observation = model.observe(query)\n",
        "    inner_step_fn(model)\n",
        "    return observation\n",
        "\n",
        "  # reusing the same parameters for the outer scan.\n",
        "  unroll_fn = nnx.scan(_step, length=outer_steps, in_axes=model_axes, out_axes=0)\n",
        "  observations = unroll_fn(model)\n",
        "  timedelta = model.dt * inner_steps\n",
        "  time = cx.LabeledAxis('timedelta', np.arange(outer_steps) * timedelta)\n",
        "  observations = cx.tag(observations, time)\n",
        "  return observations"
      ],
      "metadata": {
        "id": "kV6vyDPn6ZKm"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This results in efficient implementation of model rollouts"
      ],
      "metadata": {
        "id": "rydnU41A9KIo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trajectory = unroll_model(l96_model, 100, {'slow': {'x': k}}, 25)\n",
        "trajectory['slow']['x'].to_xarray().plot.imshow(x='k', y='timedelta')"
      ],
      "metadata": {
        "id": "aSP3ZdKB7pI8",
        "colab": {
          "height": 299
        },
        "outputId": "6a7f7adf-a333-49a9-c8fc-04fde2c3cba6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x108bd2084500>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 364
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also query Y variables"
      ],
      "metadata": {
        "id": "XQVnFprTgWzG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trajectory = unroll_model(l96_model, 100, {'fast': {'y': kj}}, 25)\n",
        "fast_ds = trajectory['fast']['y'].to_xarray().stack(k_prime=('k', 'j'))\n",
        "fast_ds.coords['k_prime'] = np.linspace(0, k.shape[0], fast_ds.sizes['k_prime'])\n",
        "fast_ds.plot.imshow(x='k_prime', y='timedelta')"
      ],
      "metadata": {
        "id": "BQuYpf5fdEfY",
        "colab": {
          "height": 300
        },
        "outputId": "b57dc790-0d8e-40d8-b0de-f47a5c6a49ac"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x108bd375c830>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 262,
              "width": 367
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Working in lower level functional API using `nnx.split`\n",
        "\n",
        "For maximum control over the computation and removing the tiny amount of overhead that comes with `nnx` wrappers it can be beneficial to drop into \"functional mode\".\n",
        "\n",
        "To do that `nnx` provides `nnx.split` and `nnx.merge` methods. For more details see [nnx-functional-api](https://flax.readthedocs.io/en/latest/nnx_basics.html#the-flax-functional-api)\n",
        "\n",
        "Since `Model` is an `nnx.Module`, we can use this machinary to enable working with `jax` transformations on parts of the state. As a basic example, we will calculate the gradient of the mean state with respect to the `h` parameter. This same approach applies to neural network parameters, which will be covered in a later tutorial."
      ],
      "metadata": {
        "id": "tsOIAGlY9O3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to compute the mean of the 'x' variable\n",
        "\n",
        "# Split the model into graph and state.\n",
        "graph, model_params, other_state = nnx.split(l96_model, nnx.Param, ...)\n",
        "\n",
        "def loss_fn(model_params):\n",
        "  # Merge the graph and state to create a local model instance for computation.\n",
        "  model = nnx.merge(graph, model_params, other_state, copy=True)\n",
        "  model.advance()\n",
        "  return jnp.mean(model.observe({'slow': {'x': k}})['slow']['x'].data)\n",
        "\n",
        "\n",
        "grad_fn = jax.grad(loss_fn)\n",
        "grads = grad_fn(model_params)\n",
        "print(f'Gradient of mean(x) with respect to params: {grads}')"
      ],
      "metadata": {
        "id": "JDNFoTzK9NUm",
        "outputId": "225b9606-379d-4011-bdc5-64d068a8e613"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gradient of mean(x) with respect to params: \u001b[38;2;79;201;177mState\u001b[0m\u001b[38;2;255;213;3m({\u001b[0m\u001b[38;2;105;105;105m\u001b[0m\n",
            "  \u001b[38;2;156;220;254m'h'\u001b[0m\u001b[38;2;212;212;212m: \u001b[0m\u001b[38;2;79;201;177mParam\u001b[0m\u001b[38;2;255;213;3m(\u001b[0m\u001b[38;2;105;105;105m # 1 (4 B)\u001b[0m\n",
            "    \u001b[38;2;156;220;254mvalue\u001b[0m\u001b[38;2;212;212;212m=\u001b[0mArray(-0.00282221, dtype=float32, weak_type=True)\n",
            "  \u001b[38;2;255;213;3m)\u001b[0m\n",
            "\u001b[38;2;255;213;3m})\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Observation operators and model queries\n",
        "\n",
        "**Key conepts:**\n",
        "* Observation operators compute predictions from prognostics to answer a user `query`\n",
        "* The model query and nested data are partitioned by an observation operator key\n",
        "* Query may contain dynamic information about the query details\n",
        "\n",
        "\n",
        "Observation operators are routinely used to build connections between simulation variables and measured data. The computation associated with this transformation is performed as a part of `model.observe` method, where queries are dispatched to observation operators.\n",
        "\n",
        "First, let's take a look at `ObservationOperator` interface and some examples:"
      ],
      "metadata": {
        "id": "rbkIobspFQrK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observation operators are `nnx.Module` instances, which allows them to hold trainable parameters and other modules that could be shared among different parts of the model.\n",
        "\n",
        "```python\n",
        "class ObservationOperator(nnx.Module, abc.ABC):\n",
        "\n",
        "  @abc.abstractmethod\n",
        "  def observe(\n",
        "      self,\n",
        "      inputs: dict[str, cx.Field],\n",
        "      query: dict[str, cx.Field | cx.Coordinate],\n",
        "  ) -> dict[str, cx.Field]:\n",
        "    \"\"\"Returns observations for `query`.\"\"\"\n",
        "    ...\n",
        "```\n",
        "\n",
        "The `observe` method computes observations associated with the `query` using `inputs` and `query` itself. In most cases `inputs` are a set of prognostic fields associated with the model that holds the observation operator, but could be other outputs as well.\n",
        "\n",
        "Lets take a look at two simple operators:\n",
        "* `DataObservationOperator`\n",
        "* `TransformObservationOperator`"
      ],
      "metadata": {
        "id": "IKz1TzuVIHSj"
      }
    },
    {
      "metadata": {
        "id": "eSROR7SvTqOq"
      },
      "cell_type": "markdown",
      "source": [
        "DataObservationOperator accepts a set of predictions at init and matches them with query, ignoring `inputs` argument."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DataObservationOperator example.\n",
        "\n",
        "x = cx.SizedAxis('x', 5)\n",
        "precomputed_predictions = {'u': cx.wrap(np.arange(5), x), 't': cx.wrap(0.0)}\n",
        "obs_op = observation_operators.DataObservationOperator(precomputed_predictions)\n",
        "ignored_inputs = {}\n",
        "print(obs_op.observe(ignored_inputs, {'u': x}))\n",
        "print(obs_op.observe(ignored_inputs, {'t': cx.Scalar()}))\n",
        "\n",
        "with print_error():\n",
        " obs_op.observe(ignored_inputs, {'v': x})"
      ],
      "metadata": {
        "id": "DWdME4-KQ6WO",
        "outputId": "ae91dc99-2640-4d0e-9cb9-9a18947623d9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'u': <Field dims=('x',) shape=(5,) axes={'x': SizedAxis} >}\n",
            "{'t': <Field dims=() shape=() axes={} >}\n",
            "ValueError: query contains k='v' not in ['u', 't']\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "NpMZ2gHUTn9q"
      },
      "cell_type": "markdown",
      "source": [
        "TransformObservationOperator applies a user-specified `transform` to provided `inputs` and uses the values to match the query."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TransformObservationOperator example.\n",
        "\n",
        "# for this example we will use lon-lat and spherical harmonic coordinates on the\n",
        "# sphere, frequently used in atmospheric simulations.\n",
        "grid = coordinates.LonLatGrid.T21()\n",
        "ylm_grid = coordinates.SphericalHarmonicGrid.T21()\n",
        "\n",
        "rng = np.random.RandomState(4)\n",
        "l, m = ylm_grid.total_wavenumbers, 2 * ylm_grid.longitude_wavenumbers\n",
        "u_modal = rng.normal(size=(m, l)) * np.exp(-4 * np.arange(l) / l)[None, :]\n",
        "modal_inputs = {'u': cx.wrap(u_modal, ylm_grid)}\n",
        "\n",
        "ylm_transform = spherical_transforms.YlmMapper(partition_schema_key=None, mesh=parallelism.Mesh())\n",
        "to_nodal_transform = transforms.ToNodal(ylm_transform)\n",
        "\n",
        "obs_op = observation_operators.TransformObservationOperator(to_nodal_transform)\n",
        "\n",
        "observation = obs_op.observe(modal_inputs, {'u': grid})\n",
        "observation['u'].to_xarray().plot(x='longitude', y='latitude')"
      ],
      "metadata": {
        "id": "r6KRVJe0SejM",
        "colab": {
          "height": 295
        },
        "outputId": "9e6c3533-b068-4181-d982-06f3a1fb1741"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.QuadMesh at 0x108bd2085640>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 373
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Users can use `TransformObservationOperator` with trainable transformations (e.g. transforms parameterized by neural networks) or implement their own `ObservationOperator` classes to suit their needs. By using custom parameter types or tags it is then possible to isolate gradients with respect to parameters of observation operators for fine-tuning tasks without affecting the rest of the model."
      ],
      "metadata": {
        "id": "p4F0FP40Uv0a"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dispatching of model query and model query structure\n",
        "\n",
        "Since `Model` can hold numerous observation operators, it needs a mechanism to dispatch a full query to individual components.\n",
        "\n",
        "This dispatch process is based on key matching between the model query and observation operators. Namely `Model` requires the following structure of operators and model query:\n",
        "`OperatorKey = str`\n",
        "* `model_query: dict[OperatorKey: dict[str, cx.Field | cx.Coordinate]]`\n",
        "* `operators: dict[OperatorKey: ObservationOperator]`\n",
        "\n",
        "This mirrors the model input data structure, which is `dict[ObservationSource, dict[str, cx.Field]]`, where `ObservationSource` string is the same as `OperatorKey`.\n",
        "\n",
        "This approach aims at simplicity at the cost of slight configuration overhead that might be encountered in cases when:\n",
        "1. multiple operators required to cover data from a single source\n",
        "2. multiple sources are associated with the same observation operator\n",
        "\n",
        "These cases can be addressed by a combination of renaming and multiple references to the same observation operator/transform. These will be covered in one of the advanced tutorials."
      ],
      "metadata": {
        "id": "EsPMGz6cV_o1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Adding observation operator to our Lorenz model\n",
        "\n",
        "To complete the intro to observation operators and queries, let us add an observation operator that looks at particular value of the state (mimicing a point measurement)"
      ],
      "metadata": {
        "id": "v0MsV_6XYsqJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sel_point = transforms.Sel({'k': 4})  # will select k == 4 from the X field.\n",
        "point_obs = observation_operators.TransformObservationOperator(sel_point)\n",
        "l96_model.operators['k=4'] = point_obs\n",
        "k4_trajectory = unroll_model(l96_model, 100, {'k=4': {'x': cx.Scalar()}}, 25)\n",
        "k4_trajectory['k=4']['x'].to_xarray().plot(x='timedelta')"
      ],
      "metadata": {
        "id": "thqegFE2UvkO",
        "colab": {
          "height": 295
        },
        "outputId": "dd8fc6fa-d7a1-471e-a6cf-24ee9d4c0c5b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x108bd90d3050>]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "height": 261,
              "width": 380
            },
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4G0TcODeNjYO"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
